We recently were invited to present internally at a prominent health care payer network about the rapidly changin role and importance of web analytics. Gone are the good old days when it was enough to just run a log analyzer or put a simple tag to collect all the information needed about the interactions a customer has with you. Analysis used to be limited in scope and focus on a handful of parameters that could be optimized, such as bounce rates and conversion rates, by tweaking the checkout flows and usability improvements.
Not that conversion rate optimization is less important today but as customer interactions focus less and less on just the company website, the new critical need is to try and get a coherent picture of general customer behavior across all touch points. Instead of trying to infer customer thoughts and concerns through their clickstreams, many are now openly expressing needs and problems through social media.
This goes beyond “cross channel marketing” into the new area Forrester and others are now calling Customer Intelligence (CI). Similar to the way business data evolved from simple reporting into Business Intelligence (BI), as customer data gets more complex and varied, putting everything together and drawing conclusions and trends from it will need to employ similar methods and tools.
This is primarily a mindset change from the somewhat passive “analytics” to the broader and much more active role of managing and providing customer intelligence.
The expectations from Web Analytics professionals and systems are changing as well from the cyclical analysis and response to the providing of on demand, immediate intelligence for both individual and aggregate customer needs and problems. In some companies this evolved into a real “command center” that has 24/7 monitoring and interaction tools to listen, interact and respond to customer needs.
There are a few challenges that mark this transition:
- Quantity: The quantity of interaction points is exploding due to social media, online videos and mobile devices.
- Traceability: It is very hard to identify users across various media. Mapping a web user to a Facebook account or twitter feed is not always possible.
- Immediacy: There is an overwhelming need and expectation for immediate response.
Here is a conceptual diagram of this new reality illustrating all the new interaction points being consolidated into the central Customer Intelligence and the introduction of the analytical services that can be used to optimize the user experience.
These analytical services can work on both an individual and aggregate level:
- Individual: If we can aggregate customer data and interactions from different channels, this will dramatically improve segmentation, insight for sales and customer service professionals interacting with the customer, and services that can target offers or content in real time based on user past interest and behavior.
Collective intelligence: By looking at customer activity across all channels we can:
- Optimize targeting through the different channels and our investment in them
- Improve recommendations
- Identify trends
- Identify problems / issues / sentiment changes and address them quickly.
To start implementing Customer Intelligence, the process is now becoming quite similar to implementing a BI solution
- Expand use of social listening and data capturing tools and store their data
- Adjust data models to accommodate multiple user identifiers, channels, devices etc.
- Redefine KPI’s
- Define and implement analytical services
Adjust reporting and analytics
- Real time
- Dashboard level
The Web Analytics vendors are starting to step up and offer tools and support for Customer Intelligence. In upcoming posts we’ll look into WebTrends, Omniture, Google and IBM to see how their offerings stack up and the type of solutions they support.